Conventional rating calculations for power cables are based on the analytical methods given by IEC standards. These methods lead to rather conservative results though, which has been acceptable in the past since high-voltage cables are normally not loaded to their thermal limits and are rather operated in cold conditions, relatively speaking. However, the power system is undergoing a transition towards increasing and more dynamic loads and, therefore, towards the need for a more flexible and resilient grid. This applies in particular to cable systems in urban areas where high-voltage cables are used due to the infeasibility of overhead lines and where the renewal or expansion of those systems is attached to high installation costs. An optimized use of existing and future cable systems is a crucial feature in order to cope with the increasing requirements. Therefore, an attempt is made to combine the advantages of the different calculation methods in order to improve performance and efficiency. Furthermore, there is high potential to improve cable calculations with the application of artificial intelligence (AI). First, however, these procedures must be brought together to be evaluated, and experience in the application of AI in cable calculations must be gathered. Therefore, an actual 400 kV cable system with a typical urban laying profile was set up in a cooperation project between Vienna’s grid operator Wiener Netze GmbH and the High Voltage Test Laboratory Graz Ltd. to develop and validate calculation methods. In addition, it is investigated which input parameters are necessary, what effect do they have on the result and which accuracy and efficiency can be achieved.
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Paper submitted for the CIGRE Session 2020, SC-B1, August 24 – September 3, 2020, online.
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Ainhirn, F., Woschitz, R., Schichler, U. et al. Extended thermal rating calculations of 400 kV XLPE cables for urban grid applications based on long-term experimental data. Elektrotech. Inftech. (2020). https://doi.org/10.1007/s00502-020-00841-6
- power cables
- thermal rating
- calculation validation
- empirical data
- artificial intelligence